Ab initio modeling of superconducting nanowire single-photon detectors

Single-photon detectors are widely used in modern communication, sensing, and computing technology. Among these detectors, superconducting nanowire single-photon detectors (SNSPDs) possess the highest detection efficiencies, the shortest timing jitter, and the lowest dark count rates. However, for several applications, including those in the biological, astronomical, and quantum computation fields, there remains a desire to push the capabilities of modern detectors even further. To realize these improvements, it is necessary to develop an understanding of the physical mechanisms underpinning single-photon detection in these devices. However, current models are phenomenological, requiring experimental data for input, or can only recover qualitative agreement, severely limiting their predictive ability. In this thesis, we begin by describing the existing theoretical frameworks used to model superconducting materials and devices, both in equilibrium and nonequilibrium. We then illustrate an example of a phenomenological approach to modeling superconducting devices by developing an electrothermal model for the superconducting nanowire cryotron and demonstrating its efficacy in predicting the DC behavior and power dissipation of the device. Finally, we expand upon the current state-of-the-art SNSPD theory by utilizing recent advances in density functional theory to develop an ab initio model for the photon detection mechanism of SNSPDs. We then validate the predictions of our model with experimental data from the literature. The resulting model requires no experimental input, provides quantitative predictions of SNSPD performance, and can be extended to describe other superconducting devices, thus enabling the possibility of conducting a systematic search of materials for enhanced device performance.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Simon, Alejandro

Advisor dc:contributor.advisor

• Berggren, Karl K.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/163720

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/163720